EmptyHeaded v. 0.1
Table of Contents
- Installing from Source
- Running Queries
EmptyHeaded is a new style of join processing engine. A full description is in our manuscript. A brief overview is as follows.
Joins are ubiquitous in data processing and often are the bottleneck of classic RDBMS workloads. Recent database theory has shown that Selinger style join optimizers, which compute joins in a pairwise fashion, are asympotically suboptimal. Therefore the join optimizers which have dominated the RDBMS for the past 40 years run suboptimal algorithms. Ngo et al. showed a new way to compute joins in a multiway fashion in this paper. EmptyHeaded extends this line of work with novel theoretical and systems advances. Namely, in our query compiler we use a new worst-case optimal join algorithm (with stronger guarantees) that leverages new theoretical advances for aggregate join queries. We are also the first to translate this new theory into a system that leverages bit-level parallelism at several granularities within the data. Our storage engine accomplishes this through its design for SIMD parallelism.
EmptyHeaded is designed to run as a python library. Behind the scenes there are three pieces of the EmptyHeaded engine.
- Query Compiler
- Code Generator
- Storage Engine
The query compiler and code generator are written in Scala and the storage engine is written in C++. The query compiler accepts a datalog string and produces a generalized hypertree decomposition (GHD) which represents our query plan. The code generator takes in the GHD from the query compiler and generates C++ library calls in the storage engine. This code is then compiled and run.
Installing from Source
To install EmptyHeaded from source ensure that your system:
- Meets all dependencies detailed below (or you are in our Docker contatiner)
- Has setup the EmptyHeaded environment
- Has compiled the QueryCompiler and Cython bindings.
Behind the scenes a lot goes on in our engine. This is no walk-in-the-park library-based engine--we have advanced theoretical compilation techniques, code generation, and highly optimized code for hardware--all spanning multiple programming languages. As such we have a fair number of dependencies. Try using our Docker images where everything is taken care of for you already.
A fundamental dependency of our system is that it is designed for machines that support the Advanced Vector Extensions (AVX) instruction set which is standard in modern and future hardware generations. Our performance is highly dependent on this instruction set being available. We currently DO NOT support old hardware generations without AVX.
- Mac or Linux operating system
- GCC 5.3 (Linux) or Apple LLVM version 7.0.2 (Mac)
- cmake 2.8 or higher (C++)
- jemalloc (C++)
- tbb (C++, Intel)
- sbt (scala)
- iPython Notebook (python)
- cython (python)
- jpype 0.6.1 (python)
- pandas (python)
The instructions below briefly describe some of our dependencies and why we have them. A complete list of our dependencies as well as how to install them is in our
Dockerfile. Note: we provide JPype and an install script in our
Why iPython Notebook?
iPython Notebook provides a easy and user-friendly front-end for users to enter, compile, and run queries.
EmptyHeaded generates code from a high level datalog description. Making generated code look nice is a challenging task! Clang-format is an easy solution.
The GNU malloc is ineffecient for multi-threaded programs. jemalloc to the rescue!
Writing an efficient parallel-sort is a challenging task. Why re-invent the wheel? Use Intel's TBB.
Pandas DataFrames provides a nice and highly used front-end for EmptyHeaded to accept tables from. We can also run without DataFrames but who doesn't love DataFrames?
Cython is awesome! It enables an easy bridge from C++ to Python, also it is just a great way to speed up Python code.
JPype is our bridge between python and java. We provide this one in our
dependencies folder along with a simple install script.
Make your life easier and use our Docker images which are always up to date.
Unfortunately iPython notebooks and Docker containers do not interact easily, but you can run standard python scripts just fine in these containers!
Two easy ways to get started in a container:
- Simply inspect our iPython notebook tutorials in this repository (can view on github) and make the corresponding python programs.
- Checkout our python test scripts in the
./test/testAll.shkick them it all off.
Setting up Environment
EmptyHeaded relies on two environment variables being set.
EMPTYHEADED_HOME the root directory for the EmptyHeaded project
EMPTYHEADED_HOME/python must be in the python search path
The easiest way to meet these dependencies is to run
source env.sh provided in the root of this repository. Note: This script will set the
The compiler needs to be compiled (which makes me wonder who compiled the first compiler?). This compiles our Scala code and a few static cython bindings.
We provide demos of EmptyHeaded in iPython notebooks.
We provide a tutorial of how to get started running your first EmptyHeaded query in the
docs/notebooks folder. In this folder is a
Getting Started Tutorial iPython notebook (amongst others) which can be loaded after executing
The syntax for all queries run in EmptyHeaded: A Relational Engine for Graph Processing can be found in
docs/notebooks/graph and the syntax for all queries run in Old Techniques for New Join Algorithms: A Case Study in RDF Processing can be found in
docs/notebooks/rdf (pipelining for LUBM 8 is still a WIP being merged to master).
A note on benchmarking: the execution time for each query can be found with our timer that outputs to the shell with
Time[Query]. Our timers seperate pure query execution time and from the time spent loading from disk for the user.